Abstract
Due to the continuous release of new products, manufacturers must design products constantly to meet the diversified and differentiated needs of customers. In order to avoid the displacement by market competitors, enterprises and manufacturers must study the multiple combinations of product shapes to design products for meeting user’s needs. At the same time, users’ consumption levels and aesthetic concepts are constantly improved, consumer demand has become more personalized and diversified, and then the development of manufacturing and information industries has made people experience material results while paying more attention to their emotional needs. As an evolutionary optimization algorithm, the individual fitness values of the interactive genetic algorithm are directly obtained from the user’s own preferences and the user could give a higher fitness degree to his favorite design individual, or directly selects his satisfied individuals as the next generation of individuals in the evolutionary process. Hence, the IGA method is used to effectively design innovative productions based on users’ emotional demand. However, the inaccurate judgment and identify of the user’s demand for product image style will increase the complexity of the design, and resulting in increased user fatigue. To respond to the challenge, this study propose a combination method of interactive genetic algorithm and fuzzy kano model (FKM) research methods, in which FKM is used to more accurately excavate the product image style that satisfies the user’s perceptual needs, thus guiding the direction of product modeling evolution, and achieving user demand-driven production evolution design. Finally, through the application of the electric bicycle case to prove the practicability and effectiveness of the method. In addition, the proposed method has increased the user satisfaction in the NPD. This method is also applicable to the styling aesthetics study for other industrial products.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A Kelly, G.: The Psychology of Personal Constructs New York. Norton, New York (1995)
Beale, R.: Supporting serendipity: using ambient intelligence to augment user exploration for data mining and web browsing. Int. J. Hum Comput Stud. 65, 421–433 (2007). https://doi.org/10.1016/j.ijhcs.2006.11.012
Brintrup, A.M., Ramsden, J., Takagi, H., Tiwari, A.: Ergonomic chair design by fusing qualitative and quantitative criteria using interactive genetic algorithms. IEEE Trans. Evol. Comput. 12, 343–354 (2008). https://doi.org/10.1109/tevc.2007.904343
Chai, C., Bao, D., Sun, L., Cao, Y.: The relative effects of different dimensions of traditional cultural elements on customer product satisfaction. Int. J. Ind. Ergon. 48, 77–88 (2015). https://doi.org/10.1016/j.ergon.2015.04.001
Chen, L.-H., Ko, W.-C.: Fuzzy approaches to quality function deployment for new product design. Fuzzy Sets Syst. 160, 2620–2639 (2009). https://doi.org/10.1016/j.fss.2008.12.003
Chuang, M.C., Chang, C.C., Hsu, S.H.: Perceptual factors underlying user preferences toward product form of mobile phones. Int. J. Ind. Ergon. 27, 247–258 (2001). https://doi.org/10.1016/s0169-8141(00)00054-8
Dawkins, R.: The blind watchmaker. J. Anim. Ecol. 16, 423–424 (1986)
Diego-Mas, J.A., Alcaide-Marzal, J.: Single users’ affective responses models for product form design. Int. J. Ind. Ergon. 53, 102–114 (2016). https://doi.org/10.1016/j.ergon.2015.11.005
Ding, M., Bai, Z.: Product color emotional design adaptive to product shape feature variation. Color Res. Appl. 44, 811–823 (2019). https://doi.org/10.1002/col.22402
Dou, R., Lin, D., Nan, G., Lei, S.: A method for product personalized design based on prospect theory improved with interval reference. Comput. Ind. Eng. 125, 708–719 (2018). https://doi.org/10.1016/j.cie.2018.04.056
Dou, R., Zhang, Y., Nan, G.: Application of combined Kano model and interactive genetic algorithm for product customization. J. Intell. Manuf. (2016). https://doi.org/10.1007/s10845-016-1280-4
Franke, N., Schreier, M., Kaiser, U.: The “I designed it myself” effect in mass customization. Manage. Sci. 56, 125–140 (2010). https://doi.org/10.1287/mnsc.1090.1077
He, L., Ming, X., Li, M., Zheng, M., Xu, Z.: Understanding customer requirements through quantitative analysis of an improved fuzzy Kano’s model. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 231, 699–712 (2017). https://doi.org/10.1177/0954405415598894
Ho, C.H., Hou, K.C.: Exploring the attractive factors of app icons. KSII Trans. Internet Inf. Syst. 9, 2251–2270 (2015). https://doi.org/10.3837/tiis.2015.06.016
Holland, J.H.: Genetic algorithms and the optimal allocation of trials. SIAM J. Comput. 2, 88–105 (1973). https://doi.org/10.1137/0202009
Hsiao, S.-W., Chiu, F.-Y., Lu, S.-H.: Product-form design model based on genetic algorithms. Int. J. Ind. Ergon. 40, 237–246 (2010). https://doi.org/10.1016/j.ergon.2010.01.009
Jaksa, R., Takagi, H.: Tuning of image parameters by interactive evolutionary computation, pp. 492–497 (2004). https://doi.org/10.1109/icsmc.2003.1243863
Ji, P., Jin, J., Wang, T., Chen, Y.: Quantification and integration of Kano’s model into QFD for optimising product design. Int. J. Prod. Res. 52, 6335–6348 (2014). https://doi.org/10.1080/00207543.2014.939777
Kano, N., Seraku, N., Takahashi, F., Tsuji, S.: Attractive quality and must-be quality. J. Jpn. Soc. Qual. Control 14, 39–44 (1984)
Kim, H.S., Cho, S.B.: Application of interactive genetic algorithm to fashion design. Eng. Appl. Artif. Intell. 13, 635–644 (2000)
Kowaliw, T., Dorin, A., Mccormack, J.: Promoting creative design in interactive evolutionary computation. IEEE Trans. Evol. Comput. 16, 523–536 (2012)
Lee, J.-H., Chang, M.-L.: Stimulating designers’ creativity based on a creative evolutionary system and collective intelligence in product design. Int. J. Ind. Ergon. 40, 295–305 (2010). https://doi.org/10.1016/j.ergon.2009.11.001
Lee, Y.-C., Huang, S.-Y.: A new fuzzy concept approach for Kano’s model. Expert Syst. Appl. 36, 4479–4484 (2009). https://doi.org/10.1016/j.eswa.2008.05.034
Lee, Y., Witell, L., Lin, S., Wang, Y.: A new Kano’s evaluation sheet. TQM J. 23, 179–195 (2011). https://doi.org/10.1108/17542731111110230
Matzler, K., Hinterhuber, H.H.: How to make product development projects more successful by integrating Kano’s model of customer satisfaction into quality function deployment. Technovation 18, 25–38 (1998). https://doi.org/10.1016/s0166-4972(97)00072-2
Meng, Q., He, L.: Fuzzy-KANO-based classification method and its application to quality attributes. Ind. Eng. J. 16, 121–125 (2013). https://doi.org/10.3969/j.issn.1007-7375.2013.03.020
Miryoku Engineering Forum: Miryoku Eng. Eng. Forum (1992)
Mok, P.Y., Xu, J., Wang, X.X., Fan, J.T., Kwok, Y.L., Xin, J.H.: An IGA-based design support system for realistic and practical fashion designs. Comput. Des. 45, 1442–1458 (2013). https://doi.org/10.1016/j.cad.2013.06.014
Nagamachi, M.: Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15, 3–11 (1995). https://doi.org/10.1016/0169-8141(94)00052-5
Poirson, E., Petiot, J.-F., Boivin, L., Blumenthal, D.: Eliciting user perceptions using assessment tests based on an interactive genetic algorithm. J. Mech. Des. 135 (2013). https://doi.org/10.1115/1.4023282
Renner, G., Ekárt, A.: Genetic algorithms in computer aided design. Comput. Des. 35, 709–726 (2003). https://doi.org/10.1016/s0010-4485(03)00003-4
Sanui, J.: Visualization of users’ requirements: introduction of the evaluation grid method. In: The 3rd Design and Decision Support Systems in Architecture and Urban Planning Conference, pp. 365–374 (1996)
Shahin, A., Barati, A., Geramian, A.: Determining the critical factors of radical innovation using an integrated model of fuzzy analytic hierarchy process-fuzzy kano with a case study in Mobarakeh steel company. EMJ - Eng. Manag. J. 29, 74–86 (2017). https://doi.org/10.1080/10429247.2017.1298182
Sheikhi Darani, Z., Kaedi, M.: Improving the interactive genetic algorithm for customer-centric product design by automatically scoring the unfavorable designs. Hum.-Centr. Comput. Inf. Sci. 7 (2017). https://doi.org/10.1186/s13673-017-0119-0
Shen, K.-S., Chang-yu, P., Lu, Y., Liu, Z., Chuang, C., Ma, M.: A study on the attractiveness of heavy duty motorcycle. World Acad. Sci. Eng. Technol. 30, 1116–1120 (2009)
Shen, K.S.: Measuring the sociocultural appeal of SNS games in Taiwan. Internet Res. 23, 372–392 (2013). https://doi.org/10.1108/10662241311331781
Shen, K.S., Chen, K.H., Liang, C.C., Pu, W.P., Ma, M.Y.: Measuring the functional and usable appeal of crossover B-car interiors. Hum. Fac. Ergon. Manuf. 25, 106–122 (2015). https://doi.org/10.1002/hfm.20525
Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89, 1275–1296 (2001). https://doi.org/10.1109/5.949485
Tsuchiya, T., Maeda, T., Matsubara, Y., Nagamachi, M.: A fuzzy rule induction method using genetic algorithm. Int. J. Ind. Ergon. 18, 135–145 (1996). https://doi.org/10.1016/0169-8141(95)00076-3
Wang, Z., Zhang, M., Sun, H., Zhu, G.: Effects of standardization and innovation on mass customization: an empirical investigation. Technovation 48–49, 79–86 (2016). https://doi.org/10.1016/j.technovation.2016.01.003
Wu, Z., Lin, T., Li, M.: A computer-aided coloring method for virtual agents based on personality impression, color harmony, and designer preference. Int. J. Ind. Ergon. 68, 327–336 (2018). https://doi.org/10.1016/j.ergon.2018.09.003
Yadav, H.C., Jain, R., Shukla, S., Avikal, S., Mishra, P.K.: Prioritization of aesthetic attributes of car profile. Int. J. Ind. Ergon. 43, 296–303 (2013). https://doi.org/10.1016/j.ergon.2013.04.008
Yoo, J.W.: A mathematical formulation for interface-based modular product design with geometric and weight constraints. Eng. Optim. 48, 985–998 (2016)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965). https://doi.org/10.1016/s0019-9958(65)90241-x
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, T., Zhou, M. (2020). New Production Development and Research Based on Interactive Evolution Design and Emotional Need. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. Mental Workload, Human Physiology, and Human Energy. HCII 2020. Lecture Notes in Computer Science(), vol 12186. Springer, Cham. https://doi.org/10.1007/978-3-030-49044-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-49044-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49043-0
Online ISBN: 978-3-030-49044-7
eBook Packages: Computer ScienceComputer Science (R0)